Analysis of Fabric Selection Stability Model Based on Big Data Algorithm for Cultural Brand Building

Zhenghao Tong; Lu He

doi:10.47297/taposatWSP2633-456915.20250601

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6卷第1期

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Analysis of Fabric Selection Stability Model Based on Big Data Algorithm for Cultural Brand Building
Analysis of Fabric Selection Stability Model Based on Big Data Algorithm for Cultural Brand Building
School of Art and Design, Fuzhou University of International Studies and Trade, Fuzhou, Fujian 350202, China
2025年6卷第1期页码：79-89
纸质出版日期：2025-01-21
DOI：10.47297/taposatWSP2633-456915.20250601
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(2025). Analysis of Fabric Selection Stability Model Based on Big Data Algorithm for Cultural Brand Building. 科技理论与实践（英文版）, 6(1), 79-89.

Tong Zhenghao,He Lu.Analysis of Fabric Selection Stability Model Based on Big Data Algorithm for Cultural Brand Building[J].Theory and Practice of Science and Technology,2025,06(01):79-89.
(2025). Analysis of Fabric Selection Stability Model Based on Big Data Algorithm for Cultural Brand Building. 科技理论与实践（英文版）, 6(1), 79-89. DOI： 10.47297/taposatWSP2633-456915.20250601.

Tong Zhenghao,He Lu.Analysis of Fabric Selection Stability Model Based on Big Data Algorithm for Cultural Brand Building[J].Theory and Practice of Science and Technology,2025,06(01):79-89. DOI： 10.47297/taposatWSP2633-456915.20250601.

摘要

Abstract

With the increase in fabric types

consumers' demand for fabrics is more personalized and unique

which requires designers to choose the right fabric match. However the traditional fabric matching method mainly relies on manual completion

this method is not only low efficiency and has a large error

but it is difficult to meet the needs of designers in a short time. Therefore

this paper combines CAD and big data technology to build a fabric matching and selection model through the combination of several algorithm models. The model improves fabric identification performance by using complex networks and SVM and improves fabric matching and selection demand prediction performance by using the Apriori algorithm and LSTM. Performance test results show that compared with other models

the proposed model exhibits higher fabric identification and classification accuracy

lower error rate and better stability in fabric matching. The experimental results show that in practical application

the matching degree of material

pattern and colour of the fabric is better than that of other models

and the demand prediction of fabric selection is more in line with the actual demand

which can provide designers with more reliable decision support.

关键词

Keywords

references

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